Recursive Selective Harmonic Elimination for Multilevel Inverters: Mathematical Formulation and Experimental Validation

A recursive method that eliminates +1 harmonics and their respective multiples from the output voltage of a cascaded H-bridge multilevel inverters with = 2 dc sources ( = 1, 2, 3,...) is proposed. It solves 2×2 linear systems with not singular matrices and always gives an exact solution with very low computational effort. Simulated results in three-phase five, nine, seventeen and thirty three level CHB inverters, and experimental results in five-level inverter demonstrate the validity of the method

Opposition-Based Quantum Bat Algorithm to Eliminate Lower-Order Harmonics of Multilevel Inverters

Selective harmonic elimination (SHE) technique is used in power inverters to eliminate specific lower-order harmonics by determining optimum switching angles that are used to generate Pulse Width Modulation (PWM) signals for multilevel inverter (MLI) switches. Various optimization algorithms have been developed to determine the optimum switching angles. However, these techniques are still trapped in local optima. This study proposes an opposition-based quantum bat algorithm (OQBA) to determine these optimum switching angles. This algorithm is formulated by utilizing habitual characteristics of bats. It has advanced learning ability that can effectively remove lower-order harmonics from the output voltage of MLI. It can eventually increase the quality of the output voltage along with the efficiency of the MLI. The performance of the algorithm is evaluated with three different case studies involving 7, 11, and 17-level three-phase MLIs. The results are verified using both simulation and experimental studies. The results showed substantial improvement and superiority compared to other available algorithms both in terms of the harmonics reduction of harmonics and finding the correct solutions

Real-Time Selective Harmonic Mitigation Technique for Power Converters Based on the Exchange Market Algorithm

Hand-in-hand with the smart-grid paradigm development, power converters used in high-power applications are facing important challenges related to efficiency and power quality. To overcome these issues, the pre-programmed Pulse-Width Modulation (PWM) methods have been extensively applied to reduce the harmonic distortion with very low power switching losses for high-power converters. Among the pre-programmed PWM techniques, Selective Harmonic Elimination (SHE) has been the prevailing solution, but recently, Selective Harmonic Mitigation (SHM) stands as a superior alternative to provide further control of the harmonic spectrum with similar losses. However, the large computational burden required by the SHM method to find a solution confines it as an off-line application, where the switching set valid solutions are pre-computed and stored in a memory. In this paper, for the first time, a real-time implementation of SHM using an off-the-shelf mid-range microcontroller is presented and tested. The Exchange Market Algorithm (EMA), initially focused on optimizing financial transactions, is considered and executed to achieve the SHM targets. The performance of the EMA-based SHM is presented showing experimental results considering a reduced number of switching angles applied to a specific three-level converter, but the method can be extrapolated to any other three-level converter topology.Ministerio de Ciencia e Innovación de España TEC2016-78430-RJunta de Andalucía P18-RT-1340Fondo Nacional de Investigación de Qatar NPRP 9-310-2-13

Electrical power prediction through a combination of multilayer perceptron with water cycle ant lion and satin bowerbird searching optimizers

Predicting the electrical power (PE) output is a significant step toward the sustainable development of combined cycle power plants. Due to the effect of several parameters on the simulation of PE, utilizing a robust method is of high importance. Hence, in this study, a potent metaheuristic strategy, namely, the water cycle algorithm (WCA), is employed to solve this issue. First, a nonlinear neural network framework is formed to link the PE with influential parameters. Then, the network is optimized by the WCA algorithm. A publicly available dataset is used to feed the hybrid model. Since the WCA is a population-based technique, its sensitivity to the population size is assessed by a trial-and-error effort to attain the most suitable configuration. The results in the training phase showed that the proposed WCA can find an optimal solution for capturing the relationship between the PE and influential factors with less than 1% error. Likewise, examining the test results revealed that this model can forecast the PE with high accuracy. Moreover, a comparison with two powerful benchmark techniques, namely, ant lion optimization and a satin bowerbird optimizer, pointed to the WCA as a more accurate technique for the sustainable design of the intended system. Lastly, two potential predictive formulas, based on the most efficient WCAs, are extracted and presented

Design and experimental implementation of voltage control scheme using the coefficient diagram method based PID controller for two-level boost converter with photovoltaic system

Introduction. Currently, in the solar energy systems and a variety of electrical applications, the power converters are essential part. The main challenge for similar systems is controller design. In the literature, the PID controller has proved its effectiveness in many industrial applications, but determining its parameters remains one of the challenges in control theory field. The novelty of the work resides in the design and experimental implementation of a two-level boost DC-DC converter controlled by a PID controller for photovoltaic (PV) maximum power extraction. Purpose. Analysis and control of the two-level boost topology with renewable energy source and design of the PID controller parameters using simple and accurate method. Methods. PID coefficients are optimized using Coefficient Diagram Method (CDM) in the MATLAB environment. Results. A mathematical model of a two-level boost converter with PID controller and PV energy source was developed and analyzed. The model allows to design the controller parameters of the proposed system. Practical value. A prototype steered by the proposed CDM-PID controller was tested using an Arduino embedded board. A comparison between the simulation results and the experimental one is presented. The obtained results illustrate that the experimental results match the simulation closely, and the proposed CDM-PID controller provides a fast and precise results.Вступ. В даний час перетворювачі потужності є невід’ємною частиною сонячних енергетичних систем та різних електричних пристроїв. Основною проблемою для таких систем є проектування контролера. У літературі ПІД-регулятор довів свою ефективність у багатьох промислових застосуваннях, але визначення його параметрів залишається однією з проблем у галузі теорії управління. Новизна роботи полягає у розробці та експериментальній реалізації дворівневого підвищувального перетворювача постійного струму, керованого ПІД-регулятором, для отримання максимальної потужності фотоелектричних пристроїв. Мета. Аналіз та управління дворівневою топологією підвищення з використанням відновлюваного джерела енергії та розрахунок параметрів ПІД-регулятора простим та точним методом. Методи. Коефіцієнти ПІД оптимізуються за допомогою методу діаграми коефіцієнтів (CDM) у середовищі MATLAB. Отримані результати. Розроблено та проаналізовано математичну модель дворівневого підвищувального перетворювача з ПІД-регулятором та фотоелектричним джерелом енергії. Модель дозволяє спроєктувати параметри контролера пропонованої системи. Практична цінність. Прототип, керований пропонованим контролером CDM-PID, протестували з використанням вбудованої плати Arduino. Наведено порівняння результатів моделювання з експериментальними даними. Отримані результати показують, що експериментальні результати близько відповідають моделюванню, а пропонований CDM-ПІД-регулятор забезпечує швидкі та точні результати

Energy Harvesting and Energy Storage Systems

This book discuss the recent developments in energy harvesting and energy storage systems. Sustainable development systems are based on three pillars: economic development, environmental stewardship, and social equity. One of the guiding principles for finding the balance between these pillars is to limit the use of non-renewable energy sources

A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics

The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area

Multi-objective power quality optimization of smart grid based on improved differential evolution

In the modern generation, Electric Power has become one of the fundamental needs for humans to survive. This is due to the dependence of continuous availability of power. However, for electric power to be available to the society, it has to pass through a number of complex stages. Through each stage power quality problems are experienced on the grid. Under-voltages and over-voltages are the most common electric problems experienced on the grid, causing industries and business firms losses of Billions of dollars each year. Researchers from different regions are attracted by an idea that will overcome all the electrical issues experienced in the traditional grid using Artificial Intelligence (AI). The idea is said to provide electric power that is sustainable, economical, reliable and efficient to the society based on Evolutionary Algorithms (EAs). The idea is Smart Grid. The research focused on Power Quality Optimization in Smart Grid based on improved Differential Evolution (DE), with the objective functions to minimize voltage swells, counterbalance voltage sags and eliminate voltage surges or spikes, while maximizing the power quality. During Differential Evolution improvement research, elimination of stagnation, better and fast convergence speed were achieved based on modification of DE’s mutation schemes and parameter control selection. DE/Modi/2 and DE/Modi/3 modified mutation schemes proved to be the excellent improvement for DE algorithm by achieving excellent optimization results with regards to convergence speed and elimination of stagnation during simulations. The improved DE was used to optimize Power Quality in smart grid in combination with the reconfigured and modified Dynamic Voltage Restorer (DVR). Excellent convergence results of voltage swells and voltage sags minimization were achieved based on application of multi-objective parallel operation strategy during simulations. MATLAB was used to model the proposed solution and experimental simulations.Electrical and Mining EngineeringM. Tech. (Electrical Engineering

A review: On path planning strategies for navigation of mobile robot

This paper presents the rigorous study of mobile robot navigation techniques used so far. The step by step investigations of classical and reactive approaches are made here to understand the development of path planning strategies in various environmental conditions and to identify research gap. The classical approaches such as cell decomposition (CD), roadmap approach (RA), artificial potential field (APF); reactive approaches such as genetic algorithm (GA), fuzzy logic (FL), neural network (NN), firefly algorithm (FA), particle swarm optimization (PSO), ant colony optimization (ACO), bacterial foraging optimization (BFO), artificial bee colony (ABC), cuckoo search (CS), shuffled frog leaping algorithm (SFLA) and other miscellaneous algorithms (OMA) are considered for study. The navigation over static and dynamic condition is analyzed (for single and multiple robot systems) and it has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches. It is also observed that the reactive approaches are used to improve the performance of the classical approaches as a hybrid algorithm. Hence, reactive approaches are more popular and widely used for path planning of mobile robot. The paper concludes with tabular data and charts comparing the frequency of individual navigational strategies which can be used for specific application in robotics